Point4D: Long-range 4D Motion Reconstruction
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Abstract (arXiv)
We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D trajectories across multi-hundred-frame videos, unlike existing 4D methods that are limited to short input windows of at most a few dozen frames. A key innovation that enables this is our flexible 3D query-based motion decoder that decouples trajectory prediction from image-plane visibility. The predicted 3D endpoints are then directly re-queried in the next chunk without re-projection or matching. Furthermore, we show that extracting and reusing a visual descriptor from an arbitrary frame where the point is visible leads to better performance than relying solely on the source patch. Overall, Point4D achieves state-of-the-art performance across diverse long-video tracking benchmarks spanning over 200 frames and largely outperforms previous feed-forward 4D method. Project page: https://point-4d.github.io
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 45 of its 50 references that are in the map. First posted on arXiv 8 Sep 2026.
Builds on
45 of 50 references.
References · 45
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33 more
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Citing papers per year: 2026: 1.
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